Overview

Dataset statistics

Number of variables18
Number of observations403776
Missing cells71286
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

REF_NO is highly overall correlated with yearHigh correlation
PM2.5 is highly overall correlated with PM10 and 2 other fieldsHigh correlation
PM10 is highly overall correlated with PM2.5 and 2 other fieldsHigh correlation
SO2 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
NO2 is highly overall correlated with PM2.5 and 4 other fieldsHigh correlation
CO is highly overall correlated with PM2.5 and 3 other fieldsHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
TEMP is highly overall correlated with O3 and 2 other fieldsHigh correlation
PRES is highly overall correlated with TEMP and 1 other fieldsHigh correlation
DEWP is highly overall correlated with TEMP and 1 other fieldsHigh correlation
year is highly overall correlated with REF_NOHigh correlation
PM2.5 has 8475 (2.1%) missing valuesMissing
PM10 has 6222 (1.5%) missing valuesMissing
SO2 has 8776 (2.2%) missing valuesMissing
NO2 has 11859 (2.9%) missing valuesMissing
CO has 20261 (5.0%) missing valuesMissing
O3 has 13007 (3.2%) missing valuesMissing
RAIN is highly skewed (γ1 = 29.4402448)Skewed
REF_NO is uniformly distributedUniform
station is uniformly distributedUniform
hour has 16824 (4.2%) zerosZeros
RAIN has 387119 (95.9%) zerosZeros
WSPM has 10891 (2.7%) zerosZeros

Reproduction

Analysis started2023-12-09 07:15:43.319593
Analysis finished2023-12-09 07:16:13.895918
Duration30.58 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

REF_NO
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct33648
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16824.5
Minimum1
Maximum33648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:13.959340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1683
Q18412.75
median16824.5
Q325236.25
95-th percentile31966
Maximum33648
Range33647
Interquartile range (IQR)16823.5

Descriptive statistics

Standard deviation9713.353
Coefficient of variation (CV)0.57733383
Kurtosis-1.2
Mean16824.5
Median Absolute Deviation (MAD)8412
Skewness0
Sum6.7933293 × 109
Variance94349226
MonotonicityNot monotonic
2023-12-09T12:46:14.065414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
< 0.1%
22444 12
 
< 0.1%
22442 12
 
< 0.1%
22441 12
 
< 0.1%
22440 12
 
< 0.1%
22439 12
 
< 0.1%
22438 12
 
< 0.1%
22437 12
 
< 0.1%
22436 12
 
< 0.1%
22435 12
 
< 0.1%
Other values (33638) 403656
> 99.9%
ValueCountFrequency (%)
1 12
< 0.1%
2 12
< 0.1%
3 12
< 0.1%
4 12
< 0.1%
5 12
< 0.1%
6 12
< 0.1%
7 12
< 0.1%
8 12
< 0.1%
9 12
< 0.1%
10 12
< 0.1%
ValueCountFrequency (%)
33648 12
< 0.1%
33647 12
< 0.1%
33646 12
< 0.1%
33645 12
< 0.1%
33644 12
< 0.1%
33643 12
< 0.1%
33642 12
< 0.1%
33641 12
< 0.1%
33640 12
< 0.1%
33639 12
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2016
105408 
2014
105120 
2015
105120 
2013
88128 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1615104
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 105408
26.1%
2014 105120
26.0%
2015 105120
26.0%
2013 88128
21.8%

Length

2023-12-09T12:46:14.161105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-09T12:46:14.237769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 105408
26.1%
2014 105120
26.0%
2015 105120
26.0%
2013 88128
21.8%

Most occurring characters

ValueCountFrequency (%)
2 403776
25.0%
0 403776
25.0%
1 403776
25.0%
6 105408
 
6.5%
4 105120
 
6.5%
5 105120
 
6.5%
3 88128
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1615104
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 403776
25.0%
0 403776
25.0%
1 403776
25.0%
6 105408
 
6.5%
4 105120
 
6.5%
5 105120
 
6.5%
3 88128
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1615104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 403776
25.0%
0 403776
25.0%
1 403776
25.0%
6 105408
 
6.5%
4 105120
 
6.5%
5 105120
 
6.5%
3 88128
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1615104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 403776
25.0%
0 403776
25.0%
1 403776
25.0%
6 105408
 
6.5%
4 105120
 
6.5%
5 105120
 
6.5%
3 88128
 
5.5%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.735378
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:14.315161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3564791
Coefficient of variation (CV)0.49833566
Kurtosis-1.157296
Mean6.735378
Median Absolute Deviation (MAD)3
Skewness-0.053269103
Sum2719584
Variance11.265952
MonotonicityNot monotonic
2023-12-09T12:46:14.393873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 35712
8.8%
5 35712
8.8%
7 35712
8.8%
8 35712
8.8%
10 35712
8.8%
12 35712
8.8%
4 34560
8.6%
6 34560
8.6%
9 34560
8.6%
11 34560
8.6%
Other values (2) 51264
12.7%
ValueCountFrequency (%)
1 26784
6.6%
2 24480
6.1%
3 35712
8.8%
4 34560
8.6%
5 35712
8.8%
6 34560
8.6%
7 35712
8.8%
8 35712
8.8%
9 34560
8.6%
10 35712
8.8%
ValueCountFrequency (%)
12 35712
8.8%
11 34560
8.6%
10 35712
8.8%
9 34560
8.6%
8 35712
8.8%
7 35712
8.8%
6 34560
8.6%
5 35712
8.8%
4 34560
8.6%
3 35712
8.8%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.748217
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:14.476426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8088915
Coefficient of variation (CV)0.55935803
Kurtosis-1.1953252
Mean15.748217
Median Absolute Deviation (MAD)8
Skewness0.0056828267
Sum6358752
Variance77.596569
MonotonicityNot monotonic
2023-12-09T12:46:14.566447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 13248
 
3.3%
2 13248
 
3.3%
28 13248
 
3.3%
27 13248
 
3.3%
26 13248
 
3.3%
25 13248
 
3.3%
24 13248
 
3.3%
23 13248
 
3.3%
22 13248
 
3.3%
21 13248
 
3.3%
Other values (21) 271296
67.2%
ValueCountFrequency (%)
1 13248
3.3%
2 13248
3.3%
3 13248
3.3%
4 13248
3.3%
5 13248
3.3%
6 13248
3.3%
7 13248
3.3%
8 13248
3.3%
9 13248
3.3%
10 13248
3.3%
ValueCountFrequency (%)
31 7776
1.9%
30 12384
3.1%
29 12672
3.1%
28 13248
3.3%
27 13248
3.3%
26 13248
3.3%
25 13248
3.3%
24 13248
3.3%
23 13248
3.3%
22 13248
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros16824
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:14.653798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9221951
Coefficient of variation (CV)0.60193001
Kurtosis-1.204174
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum4643424
Variance47.916785
MonotonicityNot monotonic
2023-12-09T12:46:14.740572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 16824
 
4.2%
1 16824
 
4.2%
22 16824
 
4.2%
21 16824
 
4.2%
20 16824
 
4.2%
19 16824
 
4.2%
18 16824
 
4.2%
17 16824
 
4.2%
16 16824
 
4.2%
15 16824
 
4.2%
Other values (14) 235536
58.3%
ValueCountFrequency (%)
0 16824
4.2%
1 16824
4.2%
2 16824
4.2%
3 16824
4.2%
4 16824
4.2%
5 16824
4.2%
6 16824
4.2%
7 16824
4.2%
8 16824
4.2%
9 16824
4.2%
ValueCountFrequency (%)
23 16824
4.2%
22 16824
4.2%
21 16824
4.2%
20 16824
4.2%
19 16824
4.2%
18 16824
4.2%
17 16824
4.2%
16 16824
4.2%
15 16824
4.2%
14 16824
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct866
Distinct (%)0.2%
Missing8475
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean79.248275
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:14.837314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q121
median55
Q3110
95-th percentile238
Maximum999
Range997
Interquartile range (IQR)89

Descriptive statistics

Standard deviation79.146708
Coefficient of variation (CV)0.99871837
Kurtosis5.728757
Mean79.248275
Median Absolute Deviation (MAD)39
Skewness1.9742865
Sum31326922
Variance6264.2014
MonotonicityNot monotonic
2023-12-09T12:46:14.937371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 8354
 
2.1%
10 6609
 
1.6%
11 6418
 
1.6%
9 6374
 
1.6%
12 6346
 
1.6%
8 6333
 
1.6%
13 5830
 
1.4%
14 5765
 
1.4%
7 5742
 
1.4%
6 5116
 
1.3%
Other values (856) 332414
82.3%
(Missing) 8475
 
2.1%
ValueCountFrequency (%)
2 7
 
< 0.1%
3 8354
2.1%
4 3221
 
0.8%
4.3 2
 
< 0.1%
4.4 1
 
< 0.1%
4.6 1
 
< 0.1%
5 3984
1.0%
6 5116
1.3%
7 5742
1.4%
7.2 1
 
< 0.1%
ValueCountFrequency (%)
999 1
< 0.1%
957 1
< 0.1%
941 1
< 0.1%
898 1
< 0.1%
882 1
< 0.1%
881 1
< 0.1%
857 1
< 0.1%
844 1
< 0.1%
826 1
< 0.1%
821 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1048
Distinct (%)0.3%
Missing6222
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean104.3279
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:15.037253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q136
median83
Q3145
95-th percentile277
Maximum999
Range997
Interquartile range (IQR)109

Descriptive statistics

Standard deviation90.1364
Coefficient of variation (CV)0.86397217
Kurtosis5.7370149
Mean104.3279
Median Absolute Deviation (MAD)52
Skewness1.8164816
Sum41475973
Variance8124.5705
MonotonicityNot monotonic
2023-12-09T12:46:15.139115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 4712
 
1.2%
5 3547
 
0.9%
18 3523
 
0.9%
14 3493
 
0.9%
16 3405
 
0.8%
17 3383
 
0.8%
13 3349
 
0.8%
20 3336
 
0.8%
24 3240
 
0.8%
21 3229
 
0.8%
Other values (1038) 362337
89.7%
(Missing) 6222
 
1.5%
ValueCountFrequency (%)
2 103
 
< 0.1%
3 719
 
0.2%
4 264
 
0.1%
5 3547
0.9%
5.4 2
 
< 0.1%
5.6 1
 
< 0.1%
6 4712
1.2%
6.4 1
 
< 0.1%
6.6 1
 
< 0.1%
7 2245
0.6%
ValueCountFrequency (%)
999 3
< 0.1%
995 1
 
< 0.1%
993 1
 
< 0.1%
992 1
 
< 0.1%
991 1
 
< 0.1%
988 1
 
< 0.1%
987 1
 
< 0.1%
986 1
 
< 0.1%
984 1
 
< 0.1%
983 1
 
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct685
Distinct (%)0.2%
Missing8776
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean15.73306
Minimum0.2856
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:15.238819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q12
median7
Q319
95-th percentile61
Maximum500
Range499.7144
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.739455
Coefficient of variation (CV)1.3817691
Kurtosis14.004989
Mean15.73306
Median Absolute Deviation (MAD)5
Skewness3.0077371
Sum6214558.7
Variance472.60392
MonotonicityNot monotonic
2023-12-09T12:46:15.339951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 97027
24.0%
3 31771
 
7.9%
4 20810
 
5.2%
5 17091
 
4.2%
6 15762
 
3.9%
7 13639
 
3.4%
8 12722
 
3.2%
9 10952
 
2.7%
10 10096
 
2.5%
11 8863
 
2.2%
Other values (675) 156267
38.7%
(Missing) 8776
 
2.2%
ValueCountFrequency (%)
0.2856 89
 
< 0.1%
0.5712 70
 
< 0.1%
0.8568 72
 
< 0.1%
1 3221
 
0.8%
1.1424 84
 
< 0.1%
1.428 94
 
< 0.1%
1.7136 83
 
< 0.1%
1.9992 110
 
< 0.1%
2 97027
24.0%
2.1 1
 
< 0.1%
ValueCountFrequency (%)
500 3
< 0.1%
411 1
 
< 0.1%
341 1
 
< 0.1%
315 1
 
< 0.1%
314 1
 
< 0.1%
310 1
 
< 0.1%
299 1
 
< 0.1%
282 1
 
< 0.1%
278 1
 
< 0.1%
277 1
 
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1209
Distinct (%)0.3%
Missing11859
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean50.352785
Minimum1.0265
Maximum290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:15.436144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0265
5-th percentile8
Q123
median43
Q371
95-th percentile116
Maximum290
Range288.9735
Interquartile range (IQR)48

Descriptive statistics

Standard deviation34.77191
Coefficient of variation (CV)0.69056577
Kurtosis1.2114205
Mean50.352785
Median Absolute Deviation (MAD)23
Skewness1.0527014
Sum19734112
Variance1209.0857
MonotonicityNot monotonic
2023-12-09T12:46:15.536816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 5572
 
1.4%
22 5556
 
1.4%
20 5523
 
1.4%
17 5467
 
1.4%
18 5441
 
1.3%
26 5420
 
1.3%
21 5416
 
1.3%
19 5368
 
1.3%
14 5366
 
1.3%
24 5358
 
1.3%
Other values (1199) 337430
83.6%
(Missing) 11859
 
2.9%
ValueCountFrequency (%)
1.0265 3
 
< 0.1%
1.2318 2
 
< 0.1%
1.4371 2
 
< 0.1%
1.6424 3
 
< 0.1%
1.8477 1
 
< 0.1%
2 4364
1.1%
2.053 1
 
< 0.1%
2.2583 3
 
< 0.1%
2.4636 1
 
< 0.1%
2.6689 2
 
< 0.1%
ValueCountFrequency (%)
290 1
< 0.1%
285 1
< 0.1%
280 1
< 0.1%
277 2
< 0.1%
273 1
< 0.1%
270 1
< 0.1%
269 1
< 0.1%
265 1
< 0.1%
264 1
< 0.1%
263 2
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct132
Distinct (%)< 0.1%
Missing20261
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1214.8433
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:15.639864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1500
median900
Q31500
95-th percentile3400
Maximum10000
Range9900
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation1124.2857
Coefficient of variation (CV)0.92545733
Kurtosis9.4502587
Mean1214.8433
Median Absolute Deviation (MAD)500
Skewness2.5606618
Sum4.6591064 × 108
Variance1264018.3
MonotonicityNot monotonic
2023-12-09T12:46:15.746319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 30662
 
7.6%
400 29849
 
7.4%
500 28043
 
6.9%
600 27189
 
6.7%
700 25720
 
6.4%
800 22728
 
5.6%
900 20655
 
5.1%
1000 19026
 
4.7%
200 17370
 
4.3%
1100 17009
 
4.2%
Other values (122) 145264
36.0%
(Missing) 20261
 
5.0%
ValueCountFrequency (%)
100 5091
 
1.3%
150 1
 
< 0.1%
200 17370
4.3%
300 30662
7.6%
350 1
 
< 0.1%
400 29849
7.4%
500 28043
6.9%
600 27189
6.7%
700 25720
6.4%
800 22728
5.6%
ValueCountFrequency (%)
10000 51
< 0.1%
9900 25
< 0.1%
9800 24
< 0.1%
9700 23
< 0.1%
9600 23
< 0.1%
9500 22
< 0.1%
9400 25
< 0.1%
9300 31
< 0.1%
9200 31
< 0.1%
9100 31
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1597
Distinct (%)0.4%
Missing13007
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean58.119327
Minimum0.2142
Maximum1071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:15.849417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q111
median45
Q383
95-th percentile180
Maximum1071
Range1070.7858
Interquartile range (IQR)72

Descriptive statistics

Standard deviation57.375966
Coefficient of variation (CV)0.98720975
Kurtosis6.0740696
Mean58.119327
Median Absolute Deviation (MAD)36
Skewness1.6351637
Sum22711231
Variance3292.0015
MonotonicityNot monotonic
2023-12-09T12:46:15.951174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 40544
 
10.0%
3 8245
 
2.0%
4 7636
 
1.9%
1 6878
 
1.7%
5 6129
 
1.5%
6 5641
 
1.4%
8 4796
 
1.2%
7 4642
 
1.1%
10 3940
 
1.0%
9 3936
 
1.0%
Other values (1587) 298382
73.9%
(Missing) 13007
 
3.2%
ValueCountFrequency (%)
0.2142 134
 
< 0.1%
0.4284 119
 
< 0.1%
0.6426 118
 
< 0.1%
0.8568 120
 
< 0.1%
1 6878
1.7%
1.071 138
 
< 0.1%
1.2852 147
 
< 0.1%
1.4994 166
 
< 0.1%
1.7136 125
 
< 0.1%
1.9278 147
 
< 0.1%
ValueCountFrequency (%)
1071 14
< 0.1%
1050 1
 
< 0.1%
1026 1
 
< 0.1%
674 1
 
< 0.1%
673 1
 
< 0.1%
500 5
 
< 0.1%
450 1
 
< 0.1%
444 1
 
< 0.1%
432 1
 
< 0.1%
429 1
 
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct1187
Distinct (%)0.3%
Missing264
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean14.088899
Minimum-19.9
Maximum41.6
Zeros2642
Zeros (%)0.7%
Negative55474
Negative (%)13.7%
Memory size3.1 MiB
2023-12-09T12:46:16.048025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19.9
5-th percentile-4
Q14
median15.4
Q323.5
95-th percentile30.7
Maximum41.6
Range61.5
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.303534
Coefficient of variation (CV)0.80230067
Kurtosis-1.0874202
Mean14.088899
Median Absolute Deviation (MAD)9.4
Skewness-0.16869784
Sum5685040
Variance127.76987
MonotonicityNot monotonic
2023-12-09T12:46:16.151332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3342
 
0.8%
1 2796
 
0.7%
0 2642
 
0.7%
2 2556
 
0.6%
-1 2436
 
0.6%
-2 2293
 
0.6%
-4 1844
 
0.5%
4 1772
 
0.4%
5 1680
 
0.4%
-5 1633
 
0.4%
Other values (1177) 380518
94.2%
ValueCountFrequency (%)
-19.9 1
< 0.1%
-19.7 1
< 0.1%
-19.5 1
< 0.1%
-18.9 1
< 0.1%
-18.7 1
< 0.1%
-18.5 1
< 0.1%
-18.1 1
< 0.1%
-17.9 1
< 0.1%
-17.4 1
< 0.1%
-17.3 1
< 0.1%
ValueCountFrequency (%)
41.6 1
 
< 0.1%
41.4 2
 
< 0.1%
41.1 3
 
< 0.1%
41 2
 
< 0.1%
40.9 1
 
< 0.1%
40.6 2
 
< 0.1%
40.5 8
< 0.1%
40.4 3
 
< 0.1%
40.3 4
< 0.1%
40.2 2
 
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct676
Distinct (%)0.2%
Missing265
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1010.2825
Minimum982.4
Maximum1042.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:16.252815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum982.4
5-th percentile994.6
Q11002
median1009.8
Q31018.3
95-th percentile1027.4
Maximum1042.8
Range60.4
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation10.356778
Coefficient of variation (CV)0.010251368
Kurtosis-0.78291954
Mean1010.2825
Median Absolute Deviation (MAD)8.2
Skewness0.15194784
Sum4.0766012 × 108
Variance107.26285
MonotonicityNot monotonic
2023-12-09T12:46:16.487962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019 2712
 
0.7%
1018 2695
 
0.7%
1021 2691
 
0.7%
1015 2602
 
0.6%
1023 2596
 
0.6%
1020 2570
 
0.6%
1017 2554
 
0.6%
1016 2528
 
0.6%
1022 2474
 
0.6%
1024 2455
 
0.6%
Other values (666) 377634
93.5%
ValueCountFrequency (%)
982.4 2
 
< 0.1%
982.7 2
 
< 0.1%
982.8 3
< 0.1%
982.9 2
 
< 0.1%
983 4
< 0.1%
983.2 4
< 0.1%
983.3 3
< 0.1%
983.4 2
 
< 0.1%
983.5 6
< 0.1%
983.6 4
< 0.1%
ValueCountFrequency (%)
1042.8 2
 
< 0.1%
1042.4 1
 
< 0.1%
1042.3 2
 
< 0.1%
1042.2 1
 
< 0.1%
1042 11
< 0.1%
1041.8 8
< 0.1%
1041.7 1
 
< 0.1%
1041.6 7
< 0.1%
1041.5 2
 
< 0.1%
1041.4 8
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct645
Distinct (%)0.2%
Missing269
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.1572914
Minimum-43.4
Maximum29.1
Zeros828
Zeros (%)0.2%
Negative168595
Negative (%)41.8%
Memory size3.1 MiB
2023-12-09T12:46:16.589206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-43.4
5-th percentile-19.4
Q1-8
median4.2
Q315.5
95-th percentile22.2
Maximum29.1
Range72.5
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation13.617273
Coefficient of variation (CV)4.3129603
Kurtosis-1.0781895
Mean3.1572914
Median Absolute Deviation (MAD)11.6
Skewness-0.25002226
Sum1273989.2
Variance185.43012
MonotonicityNot monotonic
2023-12-09T12:46:16.695013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 1559
 
0.4%
17 1519
 
0.4%
17.2 1490
 
0.4%
16.8 1483
 
0.4%
17.3 1455
 
0.4%
17.1 1445
 
0.4%
17.8 1440
 
0.4%
16.2 1429
 
0.4%
18.2 1426
 
0.4%
17.5 1409
 
0.3%
Other values (635) 388852
96.3%
ValueCountFrequency (%)
-43.4 1
 
< 0.1%
-36 1
 
< 0.1%
-35.7 1
 
< 0.1%
-35.5 1
 
< 0.1%
-35.3 7
< 0.1%
-35.1 9
< 0.1%
-35 6
< 0.1%
-34.9 2
 
< 0.1%
-34.8 7
< 0.1%
-34.6 2
 
< 0.1%
ValueCountFrequency (%)
29.1 2
 
< 0.1%
29 1
 
< 0.1%
28.8 10
< 0.1%
28.7 12
< 0.1%
28.6 2
 
< 0.1%
28.5 12
< 0.1%
28.4 14
< 0.1%
28.3 14
< 0.1%
28.2 9
< 0.1%
28.1 9
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct253
Distinct (%)0.1%
Missing261
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.067051782
Minimum0
Maximum72.5
Zeros387119
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:16.795551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72.5
Range72.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83784487
Coefficient of variation (CV)12.49549
Kurtosis1291.9083
Mean0.067051782
Median Absolute Deviation (MAD)0
Skewness29.440245
Sum27056.4
Variance0.70198402
MonotonicityNot monotonic
2023-12-09T12:46:16.895922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 387119
95.9%
0.1 3689
 
0.9%
0.2 1823
 
0.5%
0.3 1374
 
0.3%
0.4 885
 
0.2%
0.5 847
 
0.2%
0.6 698
 
0.2%
0.7 585
 
0.1%
0.9 502
 
0.1%
0.8 482
 
0.1%
Other values (243) 5511
 
1.4%
ValueCountFrequency (%)
0 387119
95.9%
0.1 3689
 
0.9%
0.2 1823
 
0.5%
0.3 1374
 
0.3%
0.4 885
 
0.2%
0.5 847
 
0.2%
0.6 698
 
0.2%
0.7 585
 
0.1%
0.8 482
 
0.1%
0.9 502
 
0.1%
ValueCountFrequency (%)
72.5 3
< 0.1%
52.1 2
 
< 0.1%
47.7 1
 
< 0.1%
46.4 6
< 0.1%
45.9 2
 
< 0.1%
41.9 1
 
< 0.1%
40.7 3
< 0.1%
39 1
 
< 0.1%
38.9 1
 
< 0.1%
37.4 2
 
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing1389
Missing (%)0.3%
Memory size3.1 MiB
NE
40049 
ENE
33262 
N
29973 
NW
29587 
E
29168 
Other values (11)
240348 

Length

Max length3
Median length2
Mean length2.2381762
Min length1

Characters and Unicode

Total characters900613
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowN
3rd rowNNW
4th rowNW
5th rowN

Common Values

ValueCountFrequency (%)
NE 40049
 
9.9%
ENE 33262
 
8.2%
N 29973
 
7.4%
NW 29587
 
7.3%
E 29168
 
7.2%
NNE 27247
 
6.7%
SW 27083
 
6.7%
NNW 24167
 
6.0%
WNW 23815
 
5.9%
ESE 23691
 
5.9%
Other values (6) 114345
28.3%

Length

2023-12-09T12:46:16.997602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 40049
 
10.0%
ene 33262
 
8.3%
n 29973
 
7.4%
nw 29587
 
7.4%
e 29168
 
7.2%
nne 27247
 
6.8%
sw 27083
 
6.7%
nnw 24167
 
6.0%
wnw 23815
 
5.9%
ese 23691
 
5.9%
Other values (6) 114345
28.4%

Most occurring characters

ValueCountFrequency (%)
N 259514
28.8%
E 246725
27.4%
W 207045
23.0%
S 187329
20.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 900613
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 259514
28.8%
E 246725
27.4%
W 207045
23.0%
S 187329
20.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 900613
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 259514
28.8%
E 246725
27.4%
W 207045
23.0%
S 187329
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 259514
28.8%
E 246725
27.4%
W 207045
23.0%
S 187329
20.8%

WSPM
Real number (ℝ)

ZEROS 

Distinct115
Distinct (%)< 0.1%
Missing238
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.7183797
Minimum0
Maximum13.2
Zeros10891
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2023-12-09T12:46:17.093340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.9
median1.4
Q32.2
95-th percentile4.2
Maximum13.2
Range13.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2379649
Coefficient of variation (CV)0.7204257
Kurtosis3.6915467
Mean1.7183797
Median Absolute Deviation (MAD)0.6
Skewness1.62527
Sum693431.5
Variance1.532557
MonotonicityNot monotonic
2023-12-09T12:46:17.200878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 21486
 
5.3%
1 21370
 
5.3%
1.2 21228
 
5.3%
0.9 20237
 
5.0%
1.3 19640
 
4.9%
0.8 18585
 
4.6%
1.4 17776
 
4.4%
0.7 16969
 
4.2%
1.5 16273
 
4.0%
1.6 15098
 
3.7%
Other values (105) 214876
53.2%
ValueCountFrequency (%)
0 10891
2.7%
0.1 4175
 
1.0%
0.2 4378
 
1.1%
0.3 2673
 
0.7%
0.4 7154
 
1.8%
0.5 10842
2.7%
0.6 13881
3.4%
0.7 16969
4.2%
0.8 18585
4.6%
0.9 20237
5.0%
ValueCountFrequency (%)
13.2 1
 
< 0.1%
12.9 1
 
< 0.1%
12.8 1
 
< 0.1%
11.8 1
 
< 0.1%
11.7 1
 
< 0.1%
11.2 3
< 0.1%
11 1
 
< 0.1%
10.9 3
< 0.1%
10.7 1
 
< 0.1%
10.5 3
< 0.1%

station
Categorical

UNIFORM 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Aotizhongxin
33648 
Changping
33648 
Dingling
33648 
Dongsi
33648 
Guanyuan
33648 
Other values (7)
235536 

Length

Max length13
Median length10.5
Mean length8.4166667
Min length6

Characters and Unicode

Total characters3398448
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAotizhongxin
2nd rowAotizhongxin
3rd rowAotizhongxin
4th rowAotizhongxin
5th rowAotizhongxin

Common Values

ValueCountFrequency (%)
Aotizhongxin 33648
8.3%
Changping 33648
8.3%
Dingling 33648
8.3%
Dongsi 33648
8.3%
Guanyuan 33648
8.3%
Gucheng 33648
8.3%
Huairou 33648
8.3%
Nongzhanguan 33648
8.3%
Shunyi 33648
8.3%
Tiantan 33648
8.3%
Other values (2) 67296
16.7%

Length

2023-12-09T12:46:17.301567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aotizhongxin 33648
8.3%
changping 33648
8.3%
dingling 33648
8.3%
dongsi 33648
8.3%
guanyuan 33648
8.3%
gucheng 33648
8.3%
huairou 33648
8.3%
nongzhanguan 33648
8.3%
shunyi 33648
8.3%
tiantan 33648
8.3%
Other values (2) 67296
16.7%

Most occurring characters

ValueCountFrequency (%)
n 639312
18.8%
i 370128
10.9%
g 370128
10.9%
a 336480
9.9%
u 302832
8.9%
o 235536
 
6.9%
h 201888
 
5.9%
l 67296
 
2.0%
D 67296
 
2.0%
y 67296
 
2.0%
Other values (16) 740256
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2994672
88.1%
Uppercase Letter 403776
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 639312
21.3%
i 370128
12.4%
g 370128
12.4%
a 336480
11.2%
u 302832
10.1%
o 235536
 
7.9%
h 201888
 
6.7%
l 67296
 
2.2%
y 67296
 
2.2%
s 67296
 
2.2%
Other values (7) 336480
11.2%
Uppercase Letter
ValueCountFrequency (%)
D 67296
16.7%
G 67296
16.7%
W 67296
16.7%
C 33648
8.3%
H 33648
8.3%
N 33648
8.3%
S 33648
8.3%
T 33648
8.3%
A 33648
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3398448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 639312
18.8%
i 370128
10.9%
g 370128
10.9%
a 336480
9.9%
u 302832
8.9%
o 235536
 
6.9%
h 201888
 
5.9%
l 67296
 
2.0%
D 67296
 
2.0%
y 67296
 
2.0%
Other values (16) 740256
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3398448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 639312
18.8%
i 370128
10.9%
g 370128
10.9%
a 336480
9.9%
u 302832
8.9%
o 235536
 
6.9%
h 201888
 
5.9%
l 67296
 
2.0%
D 67296
 
2.0%
y 67296
 
2.0%
Other values (16) 740256
21.8%

Interactions

2023-12-09T12:46:11.005003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:51.086821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:52.619210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:53.975410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:55.407820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:56.772606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:58.160836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:59.663332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:01.029455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:02.443379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:03.823461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:05.188636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:06.743258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:08.154875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:09.570939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:11.104481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:51.184393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:52.712253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:54.069605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:55.499683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:56.869141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:45:58.256295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-09T12:46:03.729620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:05.095805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:06.647600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:08.058600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:09.475106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T12:46:10.908383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-09T12:46:17.374811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
REF_NOmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINWSPMyearwdstation
REF_NO1.0000.1650.0220.001-0.066-0.078-0.289-0.075-0.048-0.043-0.0420.117-0.0260.0190.0610.9240.0690.000
month0.1651.0000.0090.000-0.004-0.043-0.1880.0590.061-0.1970.0460.0780.1910.037-0.1580.1160.0700.000
day0.0220.0091.0000.0000.0250.0390.0090.0280.011-0.0150.0150.0140.023-0.008-0.0090.0000.0230.000
hour0.0010.0000.0001.0000.0100.0630.045-0.025-0.0250.2800.146-0.037-0.012-0.0050.1640.0000.1140.000
PM2.5-0.066-0.0040.0250.0101.0000.8880.4940.6470.837-0.238-0.027-0.0620.232-0.022-0.3170.0460.0590.022
PM10-0.078-0.0430.0390.0630.8881.0000.4950.6340.729-0.197-0.025-0.0660.135-0.084-0.2390.0630.0670.036
SO2-0.289-0.1880.0090.0450.4940.4951.0000.5190.562-0.201-0.3590.292-0.351-0.151-0.0560.0990.0310.026
NO2-0.0750.0590.028-0.0250.6470.6340.5191.0000.736-0.609-0.2800.195-0.032-0.070-0.4360.0610.0910.109
CO-0.0480.0610.011-0.0250.8370.7290.5620.7361.000-0.440-0.2290.1280.0840.010-0.3820.0620.0790.040
O3-0.043-0.197-0.0150.280-0.238-0.197-0.201-0.609-0.4401.0000.598-0.4510.262-0.0110.4250.0200.1460.017
TEMP-0.0420.0460.0150.146-0.027-0.025-0.359-0.280-0.2290.5981.000-0.8130.8120.0310.1140.0820.1000.023
PRES0.1170.0780.014-0.037-0.062-0.0660.2920.1950.128-0.451-0.8131.000-0.748-0.0770.0270.1000.0610.075
DEWP-0.0260.1910.023-0.0120.2320.135-0.351-0.0320.0840.2620.812-0.7481.0000.175-0.2290.0870.0980.018
RAIN0.0190.037-0.008-0.005-0.022-0.084-0.151-0.0700.010-0.0110.031-0.0770.1751.000-0.0220.0100.0100.000
WSPM0.061-0.158-0.0090.164-0.317-0.239-0.056-0.436-0.3820.4250.1140.027-0.229-0.0221.0000.0490.1240.044
year0.9240.1160.0000.0000.0460.0630.0990.0610.0620.0200.0820.1000.0870.0100.0491.0000.0600.000
wd0.0690.0700.0230.1140.0590.0670.0310.0910.0790.1460.1000.0610.0980.0100.1240.0601.0000.119
station0.0000.0000.0000.0000.0220.0360.0260.1090.0400.0170.0230.0750.0180.0000.0440.0000.1191.000

Missing values

2023-12-09T12:46:12.583535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-09T12:46:12.965879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-09T12:46:13.679870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

REF_NOyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133104.04.04.07.0300.077.0-0.71023.0-18.80.0NNW4.4Aotizhongxin
1220133118.08.04.07.0300.077.0-1.11023.2-18.20.0N4.7Aotizhongxin
2320133127.07.05.010.0300.073.0-1.11023.5-18.20.0NNW5.6Aotizhongxin
3420133136.06.011.011.0300.072.0-1.41024.5-19.40.0NW3.1Aotizhongxin
4520133143.03.012.012.0300.072.0-2.01025.2-19.50.0N2.0Aotizhongxin
5620133155.05.018.018.0400.066.0-2.21025.6-19.60.0N3.7Aotizhongxin
6720133163.03.018.032.0500.050.0-2.61026.5-19.10.0NNE2.5Aotizhongxin
7820133173.06.019.041.0500.043.0-1.61027.4-19.10.0NNW3.8Aotizhongxin
8920133183.06.016.043.0500.045.00.11028.3-19.20.0NNW4.1Aotizhongxin
91020133193.08.012.028.0400.059.01.21028.5-19.30.0N2.6Aotizhongxin
REF_NOyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
403766336392016123114399.0412.031.0198.04900.06.03.81021.9-8.90.0SSE1.0Wanshouxigong
403767336402016123115449.0524.030.0217.05600.08.03.91021.5-6.10.0S1.4Wanshouxigong
403768336412016123116440.0440.026.0200.04700.06.02.81021.5-6.60.0SSE0.7Wanshouxigong
403769336422016123117378.0378.020.0171.03800.04.01.21021.4-5.50.0SSE1.1Wanshouxigong
403770336432016123118392.0458.014.0160.03900.03.0-1.31021.9-6.50.0S0.6Wanshouxigong
403771336442016123119449.0487.010.0153.04500.04.0-1.91022.0-6.10.0ESE0.9Wanshouxigong
403772336452016123120460.0492.012.0146.04100.04.0-2.51022.4-5.50.0ENE0.7Wanshouxigong
403773336462016123121463.0498.012.0141.04400.05.0-3.01022.1-5.30.0E0.9Wanshouxigong
403774336472016123122493.0537.012.0124.05000.08.0-3.01022.7-5.00.0SW0.1Wanshouxigong
403775336482016123123464.0490.08.0111.05400.07.0-4.01022.6-5.70.0ENE0.9Wanshouxigong